I love this ! Used to work at Rain AI on training neural networks in unconventional hardware - people often that computers don't necessarily have to be electronic digital - there is a whole domain dedicated to creating machines that can apply certain mathematical operations faster or more efficiently than their electronic counter parts. I created this site to try create a classification of that space:
They’re not competing in the same domain - if you look at their business model it actually is much closer to ML consulting for companies (CMA CGM, ASML, Airbus…). The big three are trying to capture B2C mainly while Mistral is full focused B2B
The reason we're conflating them is because there is a strong correlation between "highly processed food" and "designed recklessly". If you look at Carlos Monteiro (The pioneer in this domain) he operationalized it with the NOVA metric. NOVA 4 being the closest to what you're talking about:
"Industrially manufactured food products made up of several ingredients (formulations) including sugar, oils, fats and salt (generally in combination and in higher amounts than in processed foods) and food substances of no or rare culinary use (such as high-fructose corn syrup, hydrogenated oils, modified starches and protein isolates)..." [1]
This is what my Master project was about, working in the case of Wolof. I've trained XTTSv2 and had solid results with less than 20h of paired data that wasn't of the highest quality either - hmu: [email protected]
This is an interesting problem that has various challenges - currently most tokenization solutions where trainees using hype pair encoding where the most commonly seen combinations of letters were being selected to be a mapping. This meant that the majority of tokenization was English mappings meaning your LLM had a better tokenization of English compared to other languages it was being trained on.
https://computers.tugdual.fr/